While absolute Usefulness \(U_{a}\)  measures the gain vis-à-vis
the baseline case, relative Usefulness \(U_{r}\)  relates gain to
that of a perfect model (i.e., Eq. 5 with \(L_{m}=0\Rightarrow U_{a}=L_{b}\) ).
Usefulness functions both as a proxy for benchmarking the model (testing)
and to optimize its hyperparameters (validation). For reference, Table
REF  also reports the performance as the in text mining
widely used \(F\) -score[1]} (based on \(\mathrm {precision}=p(\mathrm {obs}=1|\mathrm {pred}=1)\) 
and \(\mathrm {recall}=p(\mathrm {pred}=1|\mathrm {obs}=1)\) ):
\(F_{\beta }=(1+\beta ^{2})\cdot \frac{\mbox{precision$\cdot $recall}}{(\beta ^{2}\cdot \mbox{precision})+\mbox{recall}}\) 
